LSH Seminar Alexander Schönhuth

Title: Efficient identification of genetic variants in single cells

Abstract: Only the analysis of the DNA or RNA of single cells, and not just the DNA or RNA of larger samples (bulks) allows to understand nature at its finest resolution. Recently introduced DNA/RNA sequencing technology has led to breakthroughs in understanding stem cell development, cancer formation and progression and immune cell differentiation that were hardly conceivable before.

The amount of DNA carried by a single cell is tiny however, which requires an experimental amplification step at the beginning of the analysis. This step introduces considerable statistical biases, which introduces non-negligible data uncertainties. These uncertainties pose tough computational challenges when aiming to identify the genetic variants inherent to single cells.

Here, we present an approach that efficiently quantifies these uncertainties, and thereby overcomes these challenges. Key to success is a statistical model that captures the conditional independencies among (both hidden and observed) variables that affect the variant identification process, and thereby points out an efficient computation scheme. Using the resulting efficient computation scheme allows for drastic improvements in comparison with other single cell variant discovery tools.